Air Pollution Scenarios towards 2030 in China: Emissions and Air Quality

Air Pollution Scenarios towards 2030 in China: Emissions and Air Quality Prof. Kebin HE Department of Environmental Science and Engineering, Tsinghua University, Beijing 100084, China Workshop on Hemispheric Transport of Air Pollution: Emission Inventories and Future projections Beijing, China October 18 th -20 th, 2006

Outlines Background Working framework Scenario development Modeling methodology Modeling results Discussion

Historical emissions Activities Energy Consumption Emissions 100000 5000 10 4 tons 10 4 vehi cl es 90000 80000 70000 60000 50000 40000 30000 20000 10000 3000 2500 2000 1500 1000 0 St eel Cement Chemical Fertilizer MTCE 1980 82 84 86 88 90 92 94 96 98 2000 2002 2004 Vehi cl e Popul at i on 2500 2000 1500 1000 500 4500 4000 3500 3000 2500 2000 1500 1000 500 0 10 4 tons fertilizer Hydr o NG oi l coal Tg 3000 2500 2000 1500 1000 500 0 0 1978 80 82 84 86 88 90 92 94 96 98 2000 02 04 1997 1998 1999 2000 2001 2002 2003 2004 2005 SO2 PM 500 0 1980 82 84 86 88 90 92 94 96 98 2000 2002 2004

Why emission projection is needed Rapid development in 10th five years, but Only Environment target is not met SO 2 : 25490Gg 19950Gg (Emitted in 2005) (2000) 18000Gg (Target in 2005) Aggressive environment target for 11th five years Projection of emissions is needed to support policy making

Work Scope Purpose: To simulate energy consumption and emission of air pollutants up to 2030 Spatial Scope: China National Level Time Step: 2000 (Base Year), 2005, 2010, 2020 and 2030 EI sectoral scope: power, industry, domestic, transportation and biomass burning Pollutants: SO 2, NO x, CO, VOC, BC and OC

Framework Energy Technology Options Environmental Technology Options Activities/Energy Use Tool: LEAP model Emissions Tool: Updated Trace-P EI Air Quality Tool: Models-3/CMAQ Energy Policy Options Environmental Policy Options

Tech based EI methodology Energy consumption Tech character Key Environmental of emission From projection LEAP Policies / tech Fuel Quality Unabated Emission Factor Activities Tech split Abatement Efficiency Implementation Rate Emission Factor Emissions

Scenario Definition BAU Sustainable Energy Scenarios towards 2020 developed by ERI Long term environmental program by government Policies and regulations to ensure sustainable development

Scenario Definition EP Policies Households energy saving Industry energy saving Building energy saving Vehicle energy efficiency improvement Indicators Energy saving lamp increases to 45% in 2010, and 70% in 2030 in urban households. 24% in 2010, and 53% in 2030 in rural households. Energy intensity decreases: Iron and steel: 1.72% per year, Nonmetal minerals: 3.2% per year, Chemical products: 3.5% per year, Manufacturing and processing: 3.5% per year, etc. Public building: Terminal heating loading (W/m 2 ) decreases to 54.4% of current value in 2010, and 32.8% in 2030. Residential building: Terminal heating loading (W/m 2 ) decreases to 55.5% of current value in 2010, and 36.1% in 2030. Energy efficiency of light buses and cars increase 87% before 2030 Indicators in BAU Urban: 34% in 2010, and 45% in 2030. Rural: 20% in 2010 and 40% in 2030 Iron and steel: 1.62% per year, Nonmetal minerals: 2.9% per year, Chemical products: 3.25% per year, Manufacturing and processing: 2.5% per year, etc. Public building: 68.4% in 2010 and 51.8% in 2030. Residential building: 70.6% in 2010 and 54.0% in 2030. Energy efficiency increase 46% before 2030

Scenario Definition PCP Policies Improvement of rural cooking condition Switching heating boilers and stoves Vehicle emission standard Two control zone policy PM control in industry Indicators Biomass consumption in cooking decreases to 50% in 2010 and 19% in 2030 Urban: 24% of heating boilers uses natural gas in 2010, and 50% in 2030. Rural: Biomass stoves contribute 50% in 2010, and 10% in 2030 Implement EURO IV in 2010, and EURO V in 2015 New power plants install FGD (flue gas desulfurization), and eliminate power plants over 30 years old. New power plants install SCR (Selective Catalytic Reduction) from 2012. Bag house installed with 20% of CFB (Circulating Fluidized Bed) boiler in 2010, and 75% in 2030; with 2% of grate furnace in 2010, and 30% in 2030 Indicators in BAU 53% in 2010 and 26% in 2030. Urban: 20% in 2010, and 45% in 2030. Rural: Biomass stoves contribute 45% in 2010, and 5% in 2030 EURO IV in 2012, and EURO V in 2018 New power plants install FGD. New power plants install SCR (Selective Catalytic Reduction) from 2015. With CFB: 15% in 2010 and 60% in 2030; with grate furnace: 1% in 2010, and 20% in 2030

Scenario Design Scenario Definition Business As Usual (BAU) Scenario I (BAU+EP) Scenario II (BAU+EP+PCP)

Development of EI Tech/Fuel options in energy consumption Power: Pulverized coal Industry: Coal/kiln, Coal/boiler (CFB, auto grate furnace, handfed grate furnace), coke, heavy oil, diesel Transportation: LDGV, LDDV, LDGT1, LDGT2, LDDT, HDGV, HDDV, MC Residential: Options of abatement tech Coal (Raw coal, briquette), biomass, LPG, NG Power: FGD, SCR Industry boiler: Fabric, ESP, wet scrubber, cyclone, none Transportation: EURO-I ~ EURO-V

Example of Emission projection: SO2 and NOx from electricity El ect r i ci t y by coal - f i r ed pl ant s (Billion gigajoule) 10 9 8 7 6 5 4 3 2 1 0 BAU Scenar i o I Scenar i o I I Capaci t y( MW) 700000 600000 500000 400000 300000 200000 100000 Coal - f i r ed pl ant s FGD i n BAU and Scenar i o I FGD in Scenario II 0 0 2001 2000 2005 2010 2020 2030 2000 2001 2005 2010 2020 2030 2000 2001 2005 2010 2020 2030 Capaci t y( MW) 700000 600000 500000 400000 300000 200000 100000 Coal - f i r ed pl ant s SCR in BAU and Scenario I SCR in Scenario II 13000 12000 11000 10000 9000 8000 7000 6000 5000 BAU S- I S- II SO2 By Power 4000 2000 2005 2010 2015 2020 2025 2030 Gg 12000 11000 10000 9000 8000 7000 6000 BAU S- I S- II Nox by Power 5000 2000 2005 2010 2015 2020 2025 2030

Emission process strategy Regional Inventory Top-down method Province-based Location of Large Point sources Urban & rural population Road network... Spatial distribution Gridding based on GIS High resolution gridded inventory Chemical speciation Temporal allocation Air quality model

Gridding Population Landcover Urban Pop. by Regions Rural Pop. by Regions Region Emis. Estimate Sector Chem. Species Road Network Shiplanes Region bndries LPSs Volcano Landcover by Regions Road by Regions Shiplane by Types LPSs by Regions Volcano by Locations Area Emis. by Fine Grid Road Emis. by Fine Grid Ship Emis. by Fine Grid LPSs Emis. Vocanic Emis. GIS Info. Alloc. Factor Emissions Figure from J.Woo

Chemical speciation VOC Source profile: Based on EPA-SPECIATE & Trace-P database Searching for local data PM Local source profile except BC & OC BC & OC: Trace-P inventory BC & OC emission from combustion: an ongoing project in our group

Temporal allocation Local temporal profile were used whenever possible Routinization of emission inventory process Finished the routine: from gridding results to model acceptable ASCII file Change from manual work in Excel to module & standard program

CMAQ Model ready emissions Showing differences in release height (SO 2 ) Upper-layer sources (power plants) Middle-layer sources (industry) Lower-layer sources (residential and transportation

Models-3/CMAQ System Framework Meteorology Processor Emission Processor Gridding Process Emission Inventory Air Quality Model PAVE Domain: 36km (164*97)

Sectoral Distribution of 2001 EI 100% 80% 60% 40% 20% others Agriculture Biomass Burning Power Generation Transport Domestic Industry 0% SO2 Nox BC OC CO NMVOC CH4 NH3 The distribution characteristics varies a lot among the different type of primary air pollutants.

Spatial Distribution of 2001 EI SO2 NOx VOC 0.3 0.3,t/(km2 year) CO

Emission Trend: SO 2 13000 12000 11000 10000 9000 8000 7000 6000 5000 BAU S- I S- II By Power 4000 2000 2005 2010 2015 2020 2025 2030 13000 12000 11000 10000 9000 8000 BAU S- I S- II 28000 26000 24000 22000 20000 By I ndust r y 18000 16000 BAU S- I S- II SO2 Emission 3500 3300 3100 2900 2700 2500 2300 2100 1900 1700 1500 BAU S- I S- II By Domest i c 2000 2005 2010 2015 2020 2025 2030 14000 30% more FGD 2000 2005 2010 2015 installation 2020 leads 2025 to 2030 less emission 15% less electricity demand (2030) leads to less emission 7000 2000 2005 2010 2015 2020 2025 2030 Ignore SO 2 Emission from transport sector

Emission Trend: NOx Gg 12000 11000 10000 9000 8000 7000 6000 BAU S- I S- II by Power Gg 24000 22000 20000 5000 S- II 2000 2005 2010 2015 2020 2025 2030 18000 BAU S- I Gg 1700 1600 NOx Emi ssi on 1500 1400 1300 1200 1100 1000 BAU S- I S- II by Domest i c 900 2000 2005 2010 2015 2020 2025 2030 Gg 7000 6500 6000 5500 5000 4500 BAU S- I S- II 16000 by I ndust r y 14000 12000 Gg 2500 2000 1500 BAU S- I S- II by Tr anspor t 10000 2000 2005 2010 2015 2020 2025 2030 4000 2000 2005 2010 2015 2020 2025 2030 1000 2000 2005 2010 2015 2020 2025 2030

Emission Trend: CO and VOC Gg CO Emi ssi on 170000 160000 150000 140000 130000 120000 110000 BAU S- I S- II 100000 2000 2005 2010 2015 2020 2025 2030 Gg 20000 19000 18000 17000 16000 15000 14000 13000 12000 11000 10000 VOC Emi ssi on BAU S- I S- II 2000 2005 2010 2015 2020 2025 2030

Emission Trend: BC and OC 1200 Gg BC Emi ssi on 1000 800 600 400 200 BAU S- I S- II 0 2000 2005 2010 2015 2020 2025 2030 Gg 4000 3500 3000 2500 2000 1500 1000 500 BAU S- I S- II OC Emi ssi on 0 2000 2005 2010 2015 2020 2025 2030

Sectoral distribution 30000 25000 kt 25000 20000 15000 10000 5000 Ot her s Bi omass Bur ni ng Tr anspor t Domest i c Fossi l Fuel s Domest i c Bi of uel s I ndust r y Power kt 20000 15000 10000 5000 Bi omass Bur ni ng Tr anspor t Domest i c Fossi l Fuel s Domest i c Bi of uel s I ndust r y Power 0 2001 2005 BAU 2010 BAU 2020 BAU 2030 BAU SO 2 0 2001 2005 BAU 2010 BAU 2020 BAU Largest emission reduction 2030 BAU NOx kt 180000 160000 140000 120000 100000 80000 60000 40000 20000 0 2001 2005 BAU 2010 BAU 2020 BAU 2030 BAU Ot her s Bi omass Bur ni ng Tr anspor t Domest i c Fossi l Fuel s Domest i c Bi of uel s I ndust r y Power CO kt 20000 18000 16000 14000 12000 10000 8000 6000 4000 2000 0 2001 2005 BAU 2010 BAU 2020 BAU 2030 BAU Bi omass Bur ni ng Tr anspor t Domest i c I ndust r y Power VOC

Sectoral distribution kt 1200 1000 800 600 400 200 0 2001 2005 BAU 2010 BAU 2020 BAU 2030 BAU Bi omass Bur ni ng Tr anspor t Domest i c I ndust r y Power kt 4000 3500 3000 2500 2000 1500 1000 500 0 2001 2005 BAU 2010 BAU 2020 BAU 2030 BAU Bi omass Bur ni ng Tr anspor t Domest i c Industry Power Gener at i on BC Largest emission reduction OC Most effective abatement technology/policy: SO 2 : FGD NOx: SCR CO, BC & OC: advanced stoves or gasification (especially, in rural)

Emission Summary Emissions in 2001 SO2 (kt) NOx (kt) BC (kt) OC (kt) CO (kt) NMVOC (kt) 20385 11347 1049 3385 155566 17441 Emissions Trends: SO2 NOx BC OC CO NMVOC 2010 BAU/2001 1.13 1.23 0.94 0.85 1.01 1.08 S 1/2001 1.06 1.18 0.82 0.76 0.96 1.04 S 2/2001 1.03 1.15 0.78 0.73 0.93 0.98 2030 BAU/2001 1.38 1.54 0.66 0.47 0.83 0.97 S 1/2001 1.15 1.33 0.49 0.38 0.75 0.87 S 2/2001 0.79 1.11 0.40 0.34 0.72 0.72

CMAQ result: SO 2 2001 2010BAU 2010 2010 2030BAU 2030 2030

CMAQ result: NO 2 2001 2010BAU 2010 2010 2030BAU 2030 2030

CMAQ result PM 2.5 2001 2010BAU 2010 2010 2030BAU 2030 2030

Discussion Technology based EI Evaluation of enforcement Regional differences of technologies

Acknowledgement U.S.EPA for founding support (ICAP, IES) ERI s 2020 China Sustainable Energy Scenario program for energy data U.S.DOE s Dr. David Streets for TRACE-P Emission Inventory U.S.EPA s Dr. Carey Jang for technical support on Medels-3/CMAQ

Thanks

Approaches to Emission Factors Chemical balance Raw gas factors (SO 2, Primary PM) Technical reports and papers Fuel characteristics, Operating practice, Control equipments Field measurement Final emission factors, Removal efficiency International EF database When lack of local measurement data (BC/OC, VOC)

Technical Split for Coal Combustion Devices Lime Good Efficiency Moderate Efficiency Poor Efficiency